The latest innovations in Social Media Advertising

Behind the scenes: modeling the social web

At CitizenNet, we have been investigating the nature of how people talk, share, link, and click on social networks. For the past two years, we have been working on a system that analyzes these relationships at scale, and sharing our results with marketing partners in a number of industries. This data analysis platform, which included a variety of natural language and semantic technologies built in-house, provided marketers amazing insight into their audience. The platform produced lots of data, visualized through graphs, charts, and tables.

Guess what? No one really cared. It turns out all the data in the world was not valuable to these marketers if they could not derive a concrete strategy from it.

The core of just about every business is to grow an active and engaged audience, and today’s social networks provide a perfect vehicle to do just that. On Facebook, in particular, a brand’s Page presence provides an easy way to communicate with an audience. By producing interesting content, Facebook allows a Page’s fans to share content with their social graph, organically growing a brand’s exposure.

But many brands need to give this organic growth a jolt, or maybe to grow into completely new segments of the market. That’s where advertising comes into play.

At CitizenNet, we decided to focus our social data analysis technology onto this computational advertisingproblem: given a core audience for a brand (or a person, place, or thing), what are other groups of people who are also likely to be interested in the same thing? If we can predict those groups accurately, we could reach them directly via Facebook’s advertising platform with pinpoint accuracy.

Initially, this problem statement seemed similar to the recommendation systems you see online. Sites such as Pandora, Netflix, and Amazon tend to use recommendation algorithms that work something like: “people who share interests with you tend to like all these other things, so we think you would too”. A common measure of such ‘likeness’ is Jaccard similarity.

CitzenNet conducted a survey of hundreds of ads, and compared their resulting click through rate (CTR) with this similarity score, calculated directly from Facebook’s reporting of the number of people who ‘Like’ something. Some of these results can be seen below:

Not much similarity. Most of the data points -- 0.02% to 0.08% CTR -- correspond to a huge range of similarity points. Clearly there is much more going on.

Beyond Jaccard similarity, we also looked at a number of other factors, such as demographics, audience size, and even Facebook’s own suggested bid. We found that there was not a straightforward relationship between these signals and CTR performance. In fact, it appeared that there was no consistent pattern for choosing these additional groups of people (for those of you more technically inclined, there is no single feature that had a R2 better than about 0.25).

The team at CitizenNet then sought to build a non-linear learning system to detect patterns between social behavior and the CTR of an ad. Over the course of over a billion impressions, the system essentially attempted to reverse-engineer what was happening between people on Facebook. Through this model, it would predict the CTR of an ad, then look at it’s actual CTR once the ads ran. And the model works! But what patterns were driving this relationship? What we discovered surprised us.

The category of the ad, semantic relationships between interests, “look-alikes” of similar audiences all played major roles, as expected. But more surprisingly, trends, sentiment and even action verb choice played significant roles. This was surprising since these factors rarely play a part in web advertising.

For example, Google matches ads to your search. Ad networks match ads to the page you are looking at, and possibly your browsing history. But all of this existing online ad technology bears only passing resemblance to Facebook. The difference? Social.

Facebook is not a portal for reading the news, and it’s not a catalog of products for sale. Rather, it is a communications platform – whether by liking, listening, voting, or any of the other actions that take place. And at the core, people communicate the things that they are interested in, and in real-time. Want to know more about that concert in the ad you saw? You may be just as inclined to ask a friend as you are to click on an ad. What other experience is like that?

At CitizenNet, we continue to model Facebook. And as Facebook matures, the actions of the social network will likely resemble how people naturally communicate and make decisions. So it seems reasonable to state we are actually modeling human nature itself. Now that’s cool! ;)

Our systems are still learning, and our engineers are still tweaking, but all this technology is tucked away behind the scenes. We made it really easy for you to leverage this power: all you need to type in is your Facebook page, and the system will do the rest.